Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add yoloshii/gigaxity-deep-research --skill research-workflowgit clone --depth 1 https://github.com/yoloshii/gigaxity-deep-researchWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/yoloshii/gigaxity-deep-research/research-workflow)<a href="https://agentmods.dev/skills/yoloshii/gigaxity-deep-research/research-workflow"><img src="https://agentmods.dev/badge/skills/yoloshii/gigaxity-deep-research/research-workflow/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/yoloshii/gigaxity-deep-research/research-workflow"><img src="https://agentmods.dev/badge/skills/yoloshii/gigaxity-deep-research/research-workflow.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 6 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Anti-Refusal · line 1275 Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
- medium Data Exfiltration · line 1645 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 1651 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 1655 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 1658 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- low Supply Chain · line 1642 Remote code is downloaded and executed. This bypasses code review and could introduce malicious code.Fix: Avoid downloading and executing remote scripts. Use trusted packages from PyPI/npm. If remote fetch is required, verify checksums and use HTTPS.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00102 | $0.22771 |
| Opus 5.5 | $0.00041 | $0.09108 |
| Sonnet 5.5 | $0.00020 | $0.04554 |
| Haiku 4.5 | $0.00010 | $0.02277 |
Grade A, and why
research-workflow scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s -o /dev/null -w "%{http_code}" -L "https://github.com/<owner>/<repo>" How it starts
The opening of the file, as written. The whole thing — 1,712 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Workflow Skill
Overview
This skill orchestrates research queries using the optimal workflow based on query type. It integrates:
- Triple Stack: Context7 (docs) + Exa (code/web) + Jina (web/academic/parallel)
- gigaxity-deep-research: synthesis engine over any OpenAI-compatible chat-completions endpoint (self-hosted vLLM/SGLang/llama.cpp on the
local-inferencebranch, OpenRouter onmain) - exa-answer: speed-critical 1–2 s factual lookups
- brightdata_fallback: blocked-URL recovery (CAPTCHA / paywall / Cloudflare)
- gptr-mcp: social-first research over Reddit, X/Twitter, YouTube — wraps GPT Researcher
Tool Schema Loading (MANDATORY)
MCP tool schemas are deferred. Bare mcp__X__Y(...) calls fail with InputValidationError because the schema isn't loaded. Load schemas first via ToolSearch:
ToolSearch(query='select:mcp__context7__resolve-library-id,mcp__context7__query-docs') # docs (two-step)
ToolSearch(query='select:mcp__exa__web_search_exa,mcp__jina__read_url') # multiple
ToolSearch(query='+exa-answer') # keyword (rank by relevance)
After ToolSearch returns the <function>... block for a tool, that tool is callable for the rest of the session — no need to re-load.
Why this matters: if you skip ToolSearch and the bare call fails, the path of least resistance is to fall through to WebFetch / WebSearch — neither is in the Triple Stack. Using them is the strongest signal that schema loading was skipped.
❌ mcp__context7__query-docs(libraryId="...", query="...") # fails — schema not loaded
✅ ToolSearch(query='select:mcp__context7__resolve-library-id,mcp__context7__query-docs')
→ then mcp__context7__resolve-library-id(...) → mcp__context7__query-docs(...) # works
❌ Tool fails silently → fall back to WebFetch
✅ Tool fails → check whether schema was loaded → ToolSearch + retry
Subagents inherit the same deferred-loading discipline — when spawning a research subagent via the Task tool, the subagent prompt MUST include ToolSearch(query='select:...') ahead of every mcp__X__Y reference, otherwise the subagent will fall through to WebFetch the same way.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago Changed · +1 lines 35e6be6422db
- 2mo ago First seen · 1,711 lines · 102 tokens per session scan A 5315809b8374
research-workflow is a skill published in the GitHub repository yoloshii/gigaxity-deep-research (65 stars, last pushed 1mo ago), licensed MIT. It adds 102 tokens to every session and 22,771 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
research-workflow compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| research-workflowyoloshii/gigaxity-deep-research | Skill | 65 | 1mo ago | MIT | A | 102 | Claude Code, Codex |
| worker-integrationruvnet/ruflo | Skill | 81k | yesterday | MIT | A | 14 | Claude Code |
| agentmailpaperclipai/paperclip | Skill | 111k | yesterday | MIT | A | 30 | Claude Code, Codex |
| foundry-config-setupmicrosoft/agent-framework | Skill | 15k | yesterday | MIT | A | 65 | Claude Code, Codex |
| jetson-diagnosticNVIDIA/skills | Skill | 3.9k | yesterday | Apache-2.0 | A | 30 | Claude Code, Codex |
| nemo-automodel-launcher-configNVIDIA/skills | Skill | 3.9k | yesterday | Apache-2.0 | A | 30 | Claude Code |
| mochi-remindkirodotdev/KiroCrew | Skill | 4.8k | yesterday | Apache-2.0 | A | 20 | Claude Code, Codex |